Take Home Exercise 2 - Regionalization of Nigeria with Water points

Author

Allan Chong

Overview

Water is a crucial resource for humanity. People must have access to clean water in order to be healthy. It promotes a healthy environment, peace and security, and a sustainable economy. However, more than 40% of the world’s population lacks access to enough clean water. According to UN-Water, 1.8 billion people would live in places with a complete water shortage by 2025. One of the many areas that the water problem gravely threatens is food security. Agriculture uses over 70% of the freshwater that is present on Earth.

The severe water shortages and water quality issues are seen in underdeveloped countries. Up to 80% of infections in developing nations are attributed to inadequate water and sanitation infrastructure.

Despite technological advancement, providing rural people with clean water continues to be a key development concern in many countries around the world, especially in those on the continent of Africa.

The spatial patterns of non-functional water points will be shown in this study by using the proper global and local spatial association methodologies. We look at Nigeria’s in this assignment.

Getting Started

First, we load the required packages in R

Spatial data handling
- sf, rgdal and spdep
Attribute data handling
- knitr, tidyverse, funModeling especially readr, ggplot2 and dplyr
Choropleth mapping
- tmap
Multivariate data visualization and analysis
- coorplot, ggpubr, and heatmaply
Cluster analysis
- cluster
- ClustGeo

pacman::p_load(knitr, rgdal, spdep, tmap, sf, 
               ggpubr, cluster, funModeling,
               factoextra, NbClust, #factoextra factor analysis, access clustering results
               heatmaply, corrplot, psych, tidyverse)

Spatial Data

The spatial dataset used in this assignment is the Nigeria Level-2 Administrative Boundary spatial dataset downloaded from Center for Humanitarian Data - Nigeria - Subnational Administrative Boundaries

We will load the spatial features by using st_read() from the sf package

As the data we want is in WSG-84 format, we set crs to 4326.

We won’t utilize st_transform() at this time because it can result in outputs with missing points after transformation, which would skew our study.

nga = st_read(dsn = "data/geospatial",
               layer = "nga_admbnda_adm2_osgof_20190417",
               crs = 4326)

Reading layer `nga_admbnda_adm2_osgof_20190417' from data source 
  `D:\Allanckw\ISSS624\Take-Home_Ex2\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 774 features and 16 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS:  WGS 84

#nigeria_lga_sf = st_transform(nigeria_lga_sf, crs=4326) cause missing points

We could use st_crs()to verify the coordinate system from the object.

st_crs(nga)

Coordinate Reference System:
  User input: EPSG:4326 
  wkt:
GEOGCRS["WGS 84",
    ENSEMBLE["World Geodetic System 1984 ensemble",
        MEMBER["World Geodetic System 1984 (Transit)"],
        MEMBER["World Geodetic System 1984 (G730)"],
        MEMBER["World Geodetic System 1984 (G873)"],
        MEMBER["World Geodetic System 1984 (G1150)"],
        MEMBER["World Geodetic System 1984 (G1674)"],
        MEMBER["World Geodetic System 1984 (G1762)"],
        MEMBER["World Geodetic System 1984 (G2139)"],
        ELLIPSOID["WGS 84",6378137,298.257223563,
            LENGTHUNIT["metre",1]],
        ENSEMBLEACCURACY[2.0]],
    PRIMEM["Greenwich",0,
        ANGLEUNIT["degree",0.0174532925199433]],
    CS[ellipsoidal,2],
        AXIS["geodetic latitude (Lat)",north,
            ORDER[1],
            ANGLEUNIT["degree",0.0174532925199433]],
        AXIS["geodetic longitude (Lon)",east,
            ORDER[2],
            ANGLEUNIT["degree",0.0174532925199433]],
    USAGE[
        SCOPE["Horizontal component of 3D system."],
        AREA["World."],
        BBOX[-90,-180,90,180]],
    ID["EPSG",4326]]

Before we start analyzing the data, lets us take a look at some characteristics of the spatial features to have a sense of what we are dealing with. We can use glimpse() to determine to accomplish that

glimpse(nga)

Rows: 774
Columns: 17
$ Shape_Leng <dbl> 0.2370744, 0.2624772, 3.0753158, 2.5379842, 0.6871498, 1.06…
$ Shape_Area <dbl> 0.0015239210, 0.0035311037, 0.3268678399, 0.0683785064, 0.0…
$ ADM2_EN    <chr> "Aba North", "Aba South", "Abadam", "Abaji", "Abak", "Abaka…
$ ADM2_PCODE <chr> "NG001001", "NG001002", "NG008001", "NG015001", "NG003001",…
$ ADM2_REF   <chr> "Aba North", "Aba South", "Abadam", "Abaji", "Abak", "Abaka…
$ ADM2ALT1EN <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ ADM2ALT2EN <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ ADM1_EN    <chr> "Abia", "Abia", "Borno", "Federal Capital Territory", "Akwa…
$ ADM1_PCODE <chr> "NG001", "NG001", "NG008", "NG015", "NG003", "NG011", "NG02…
$ ADM0_EN    <chr> "Nigeria", "Nigeria", "Nigeria", "Nigeria", "Nigeria", "Nig…
$ ADM0_PCODE <chr> "NG", "NG", "NG", "NG", "NG", "NG", "NG", "NG", "NG", "NG",…
$ date       <date> 2016-11-29, 2016-11-29, 2016-11-29, 2016-11-29, 2016-11-29…
$ validOn    <date> 2019-04-17, 2019-04-17, 2019-04-17, 2019-04-17, 2019-04-17…
$ validTo    <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ SD_EN      <chr> "Abia South", "Abia South", "Borno North", "Federal Capital…
$ SD_PCODE   <chr> "NG00103", "NG00103", "NG00802", "NG01501", "NG00302", "NG0…
$ geometry   <MULTIPOLYGON [°]> MULTIPOLYGON (((7.401109 5...., MULTIPOLYGON (…

We can use freq() of the funModeling package to display the distribution of Level 1 administration (Which are states in Nigeria) instead and only zooming in on the micro level when we perform the water point analysis.

freq(data=nga, input = 'ADM1_EN')

                     ADM1_EN frequency percentage cumulative_perc
1                       Kano        44       5.68            5.68
2                    Katsina        34       4.39           10.07
3                        Oyo        33       4.26           14.33
4                  Akwa Ibom        31       4.01           18.34
5                       Osun        30       3.88           22.22
6                      Borno        27       3.49           25.71
7                        Imo        27       3.49           29.20
8                     Jigawa        27       3.49           32.69
9                      Delta        25       3.23           35.92
10                     Niger        25       3.23           39.15
11                     Benue        23       2.97           42.12
12                    Kaduna        23       2.97           45.09
13                    Rivers        23       2.97           48.06
14                    Sokoto        23       2.97           51.03
15                   Adamawa        21       2.71           53.74
16                   Anambra        21       2.71           56.45
17                     Kebbi        21       2.71           59.16
18                      Kogi        21       2.71           61.87
19                    Bauchi        20       2.58           64.45
20                     Lagos        20       2.58           67.03
21                      Ogun        20       2.58           69.61
22               Cross River        18       2.33           71.94
23                       Edo        18       2.33           74.27
24                      Ondo        18       2.33           76.60
25                      Abia        17       2.20           78.80
26                     Enugu        17       2.20           81.00
27                   Plateau        17       2.20           83.20
28                      Yobe        17       2.20           85.40
29                     Ekiti        16       2.07           87.47
30                     Kwara        16       2.07           89.54
31                    Taraba        16       2.07           91.61
32                   Zamfara        14       1.81           93.42
33                    Ebonyi        13       1.68           95.10
34                  Nasarawa        13       1.68           96.78
35                     Gombe        11       1.42           98.20
36                   Bayelsa         8       1.03           99.23
37 Federal Capital Territory         6       0.78          100.00

774 Local Government Areas (LGA) make up Nigeria’s 37 states, with Kano having the most LGAs overall.

For a meaningful analysis, there are just too many LGAs, both large and little.

Calling ttm() in the tmap package will switch the tmap’s viewing mode to interactive viewing, which will help us better visualize the map. Without this change, the generated map will be too small for any type of analysis. Additionally, we’ll base the map’s plot on States (Level 1 Administration Area)

Given that there are 37 states, we must raise the maximum number of categories from the default value of 30 to 37. Using tmap_options(max.categories = 37), the threshold can be set.

ttm()
tmap_options(max.categories = 37)

Now, we are ready to build our map with the functions in the tmap package

tm_shape(nga) + 
 
  tm_polygons("ADM1_EN") +
  tm_borders(alpha=0.5) + 
  tm_scale_bar() +
  tm_grid (alpha=0.2) +
  tm_layout(main.title="Map of Nigeria LGA", 
            main.title.position="center", 
            main.title.size=1.2, 
            legend.height = 0.35, 
            legend.width = 0.35, 
            frame = TRUE)

Aspatial Data

Cleaning the Data

The aspatial dataset used in this assignment is the water point data exchange dataset found in WPdx Global Data Repositories. Data is filtered on the web portal to only keep Nigeria and the file is saved as NigeriaWaterPoints_Raw.csv

As we are only interested in the functionality of the water point, it is important to capture fields that may affect the functionality

LGA: The area we are interested in
State: The state of the LGA of Nigeria
Functional: Whether it is functional or not
management: who manages it?
Quality: what is the quality?
Water Source Category: where the water came from?
Water Tech Category: What technology is used?
latitude
longitude

To load the raw data file, we use the read_csv function

wpdx_raw = read_csv("data/aspatial/NigeriaWaterPoints_Raw.csv")

Most of the columns are irrelevant, so we will perform the following:

keep the columns we want to clean it up by specifying the columns with one to retain with subset
renaming the columns using rename_with
Replace all the NA with unknown for columns with NA value present

retain_cols = c('#clean_adm2', '#clean_adm1', '#status_clean', '#management_clean', '#subjective_quality', '#fecal_coliform_presence', '#water_source_category', '#water_tech_category', '#lat_deg', '#lon_deg' )

new_col_names = c('LGA', 'State', 'Functional', 'Management', 'Quality', 'presence_of_fecal_coliform', 'Water_Source_Category', 'Water_Tech_Category', 'latitude', 'longitude')

wpdx_clean = subset(wpdx_raw, select = (names(wpdx_raw) %in% retain_cols)) %>%  rename_with(~ new_col_names, all_of(retain_cols)) %>% 
replace_na(list(Functional = "Unknown", Management = "Unknown", Quality = "Unknown", Water_Source_Category = "Unknown", Water_Tech_Category = "Unknown"))

We save the clean file with saveRDS(), the file will be reduced to 1.6MB from the 144MB raw file that we downloaded.

saveRDS(wpdx_clean, "data/aspatial/wpdx_clean.rds")

We can then delete the raw file from the project and retrieve the saved RDS file using readRDS()

wpdx_clean = readRDS("data/aspatial/wpdx_clean.rds")

Converting csv data into spatial features

We can use st_as_sfto create a dataframe from the longitude (x) and latitude (y) values. The EPSG 4326 code is used as the dataset is referencing WGS84 geographic coordinate system. We could use st_crs()to verify the coordinate system from the object.

wpdx_clean_sf = st_as_sf(wpdx_clean, coords = c("longitude", "latitude"), crs=4326)
st_crs(wpdx_clean_sf)

Coordinate Reference System:
  User input: EPSG:4326 
  wkt:
GEOGCRS["WGS 84",
    ENSEMBLE["World Geodetic System 1984 ensemble",
        MEMBER["World Geodetic System 1984 (Transit)"],
        MEMBER["World Geodetic System 1984 (G730)"],
        MEMBER["World Geodetic System 1984 (G873)"],
        MEMBER["World Geodetic System 1984 (G1150)"],
        MEMBER["World Geodetic System 1984 (G1674)"],
        MEMBER["World Geodetic System 1984 (G1762)"],
        MEMBER["World Geodetic System 1984 (G2139)"],
        ELLIPSOID["WGS 84",6378137,298.257223563,
            LENGTHUNIT["metre",1]],
        ENSEMBLEACCURACY[2.0]],
    PRIMEM["Greenwich",0,
        ANGLEUNIT["degree",0.0174532925199433]],
    CS[ellipsoidal,2],
        AXIS["geodetic latitude (Lat)",north,
            ORDER[1],
            ANGLEUNIT["degree",0.0174532925199433]],
        AXIS["geodetic longitude (Lon)",east,
            ORDER[2],
            ANGLEUNIT["degree",0.0174532925199433]],
    USAGE[
        SCOPE["Horizontal component of 3D system."],
        AREA["World."],
        BBOX[-90,-180,90,180]],
    ID["EPSG",4326]]

We can then use glimpse() to verify each field’s data type & available values.

There are 95, 008 water points in the LGAs. The results also shows that the longitude and latitude values have been converted to a geometry object consisting of the longitude and latitude values as points, with both columns now dropped.

glimpse(wpdx_clean_sf)

Rows: 95,008
Columns: 8
$ Water_Source_Category <chr> "Unknown", "Well", "Well", "Well", "Well", "Well…
$ Water_Tech_Category   <chr> "Tapstand", "Mechanized Pump", "Hand Pump", "Unk…
$ State                 <chr> "Ekiti", "Ogun", "Ebonyi", "Enugu", "Enugu", "Be…
$ LGA                   <chr> "Moba", "Obafemi-Owode", "Ohaukwu", "Isi-Uzo", "…
$ Management            <chr> "Unknown", "Other", "Unknown", "Unknown", "Unkno…
$ Functional            <chr> "Unknown", "Functional", "Unknown", "Unknown", "…
$ Quality               <chr> "Unknown", "Acceptable quality", "Unknown", "Unk…
$ geometry              <POINT [°]> POINT (5.12 7.98), POINT (3.597668 6.96453…

Aggregate the Data

We can use freq() of the funModeling package to display the distribution of functional field in wpdx_clean_sf. This is to help us aggregate the data as the dataset provide breakdowns of functional status. In order to only look at non functional water points, we will need to aggregate the different categories into simply functional, non functional and unknowns.

freq(data=wpdx_clean_sf, input = 'Functional')

                        Functional frequency percentage cumulative_perc
1                       Functional     45883      48.29           48.29
2                   Non-Functional     29385      30.93           79.22
3                          Unknown     10656      11.22           90.44
4      Functional but needs repair      4579       4.82           95.26
5 Non-Functional due to dry season      2403       2.53           97.79
6        Functional but not in use      1686       1.77           99.56
7         Abandoned/Decommissioned       234       0.25           99.81
8                        Abandoned       175       0.18           99.99
9 Non functional due to dry season         7       0.01          100.00

To aggregate them into functional, non functional and unknown, we will create new data frames to store them by using the filter function

func_list = c("Functional", "Functional but needs repair", "Functional but not in use")
wpt_functional = wpdx_clean_sf %>%
  filter(Functional %in% func_list)

wpt_non_functional = wpdx_clean_sf %>%
  filter(!Functional %in% c(func_list, "Unknown"))

wpt_unknown = wpdx_clean_sf %>%
  filter(Functional %in% "Unknown")

Combining Spatial & Aspatial Data

We can use st_intersects() to find common data points between geographical datasets. In our case we need to find the common points in the Nigeria’s LGA spatial dataset and the water point aspatial dataset

The below code does 4 things

It intersects the Nigeria LGA dataset (nga dataframe) with the water point dataset (wpdx_clean_sf dataframe) and produce a new column to denote the total number of water points in the area (Total wpt) by using mutate() and lengths()
Similar to step 1, the result of step 1 is piped to add 3 columns to denote the number of functional, non functional and unknown water points in the area to produce wpt functional, wpt non functional and wpt unknown respectively
We also add 2 new columns to find the percentage of functional and non functional water points by using mutate()
Select appropriate columns required which are the LGA area and LGA code (Column 3 & 4), Administration Level 1 Area and Administration Level 1 Code (Column 8 & 9) which represent states, the columns that were added as explained in steps 2 & 3 and the geometry multipolygon objects (Column 18 to 23) using select()

nga_wp = nga %>% 
  #combine nga with water point sf
  mutate(`total wpt` = lengths(
    st_intersects(nga, wpdx_clean_sf))) %>%
  #add columns to produce no. of functional, non functional and unknown points
  mutate(`wpt functional` = lengths(
    st_intersects(nga, wpt_functional))) %>%
  mutate(`wpt non functional` = lengths(
    st_intersects(nga, wpt_non_functional))) %>%
  mutate(`wpt unknown` = lengths(
    st_intersects(nga, wpt_unknown))) %>%
  
  #add columns to compute %
  mutate(pct_functional = `wpt functional`/`total wpt`) %>%
  mutate(`pct_non-functional` = `wpt non functional`/`total wpt`) %>%
  select(3:4, 8:9, 18:23)

Updating Spatial features that have identical name but are in different states

The following code determines whether any LGA names have been repeated. If the shapeName is not unique for rows, duplicated() returns True. Only rows that satisfy the duplicated rows = True criterion are returned by subset() (Loh, 2022).

subset(nga_wp, duplicated(nga_wp$ADM2_EN))$ADM2_EN

[1] "Bassa"    "Ifelodun" "Irepodun" "Nasarawa" "Obi"      "Surulere"

From the result, there are 6 LGAs that have the same names, even though they are from different states. We will want to rename them or it would be confusing when we conduct further analysis.

nga_wp %>% filter(grepl("Bassa",ADM2_EN))

Simple feature collection with 2 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 6.699304 ymin: 7.441895 xmax: 8.978167 ymax: 10.39507
Geodetic CRS:  WGS 84
  ADM2_EN ADM2_PCODE ADM1_EN ADM1_PCODE total wpt wpt functional
1   Bassa   NG023004    Kogi      NG023       126             52
2   Bassa   NG032002 Plateau      NG032       199             67
  wpt non functional wpt unknown pct_functional pct_non-functional
1                 74           0      0.4126984          0.5873016
2                 50          82      0.3366834          0.2512563
                        geometry
1 MULTIPOLYGON (((7.445787 8....
2 MULTIPOLYGON (((8.823522 10...

Based on the values, we know the ADM2_PCODE of the respective Bassa are NG023004 (Kogi) and NG032002 (Plateau), so we will replace them using the code below.

nga_wp$ADM2_EN[nga_wp$ADM2_PCODE=="NG023004"] = 'Bassa (Kogi)'
nga_wp$ADM2_EN[nga_wp$ADM2_PCODE=="NG032002"] = 'Bassa (Plateau)'

We repeat the same for the rest of the duplicates and we have the final results as follow

nga_wp$ADM2_EN[nga_wp$ADM2_PCODE=="NG024005"] = "Ifelodun (Kwara)"
nga_wp$ADM2_EN[nga_wp$ADM2_PCODE=="NG030016"] = "Ifelodun (Osun)"

nga_wp$ADM2_EN[nga_wp$ADM2_PCODE=="NG024009"] = "Irepodun (Kwara)"
nga_wp$ADM2_EN[nga_wp$ADM2_PCODE=="NG030020"] = "Irepodun (Osun)"

nga_wp$ADM2_EN[nga_wp$ADM2_PCODE=="NG020031"] = "Nasarawa (Kano)"
nga_wp$ADM2_EN[nga_wp$ADM2_PCODE=="NG026009"] = "Nasarawa (Nasarawa)"

nga_wp$ADM2_EN[nga_wp$ADM2_PCODE=="NG007014"] = "Obi (Benue)"
nga_wp$ADM2_EN[nga_wp$ADM2_PCODE=="NG026011"] = "Obi (Nasarawa)"

nga_wp$ADM2_EN[nga_wp$ADM2_PCODE=="NG025020"] = "Surulere (Lagos)"
nga_wp$ADM2_EN[nga_wp$ADM2_PCODE=="NG031033"] = "Surulere (Oyo)"

We then change the row names to the names of the LGAs

row.names(nga_wp) <- nga_wp$ADM2_EN

Visualizing the spatial distribution of water points

We could use breaks of the summary statistics by using percentiles, this is to help us find out the distribution of water points in each quantile.

summary(nga_wp$`wpt non functional`)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00   12.25   34.00   41.60   60.75  278.00

It is recommended not to use the default style with breaks as quantile since the range from the third quantile to the maximum is too wide and could result in a skewed representation. We compute the variance and standard deviation of non-functional water points first to better understand our dataset since we now need to decide which style is appropriate for the map.

var(nga_wp$`wpt non functional`)

[1] 1376.914

sd(nga_wp$`wpt non functional`)

[1] 37.10679

It appears that this dataset has a very large variance. Since the variance is so high, we would like to lower it. Using the kmeans style is one method to do this. n = 6 is choosen as after some experimentation, it appears that 6 is the optimal number of clusters.

Functions from the tmap packages is used to produce the map

First we use tm_shape() + tm_fill("ADM1_EN") to form Layer 1 of the map to form the 37 states of the map. The Pastel1 palette is used because it is difficult to read different shades of the same two to three colors; Pastel1 has more colors, making states more distinct.

Next we use tm_shape() + tm_fill("wpt non functional") to form Layer 2 of the map which are the non functional water points. The palette used in this case is Purple Red such that areas with very little water points are shaded with a very light colour.

We may switch between layers on the interactive map to superimpose the nonfunctional water locations. With so many polygons, putting it side by side can be challenging to interpret.

tm_shape(nga) + 
 
  tm_fill("ADM1_EN", palette = "Pastel1") +
  tm_borders(alpha=0.5) + 
  tm_grid (alpha=0.2) +

  tm_shape(nga_wp) + 
  tm_fill("wpt non functional", 
          palette ="PuRd", style="kmeans", n=6) +  
  tm_borders(alpha=0.5) + 
  tm_grid (alpha=0.2) +
  tm_layout(main.title="non functional WP - 2 Layer map", 
            main.title.position="center", 
            main.title.size=1.2, 
            #legend.height = 0.35, 
            #legend.width = 0.35, 
            frame = TRUE)

Using dplyr package, we can summarize find out which States has the most number non functional water points and which are the states that has the most number of LGAs by using the functions group_by, summarise and arrange

#Sum of non functional water points 
nga_wp %>% 
  group_by(ADM1_EN) %>% 
   summarise(NF_Frequency = sum(`wpt non functional`), 
             #F_Frequency = sum(`wpt functional`),
             Total_Freq = sum(`total wpt`),
             NF_Ratio = (NF_Frequency / Total_Freq) * 100
             ) %>% 
    arrange(desc(NF_Frequency))

Simple feature collection with 37 features and 4 fields
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS:  WGS 84
# A tibble: 37 × 5
   ADM1_EN     NF_Frequency Total_Freq NF_Ratio                         geometry
   <chr>              <int>      <int>    <dbl>                   <GEOMETRY [°]>
 1 Osun                2118       5519     38.4 POLYGON ((4.910021 7.841812, 4.…
 2 Kaduna              1912       4925     38.8 POLYGON ((8.273597 11.30846, 8.…
 3 Kwara               1634       3531     46.3 POLYGON ((4.876071 9.157646, 4.…
 4 Kano                1570       7125     22.0 POLYGON ((8.727456 12.21461, 8.…
 5 Ondo                1552       2575     60.3 POLYGON ((5.937628 7.648777, 5.…
 6 Katsina             1521       5465     27.8 POLYGON ((8.3992 13.0758, 8.390…
 7 Jigawa              1517       9696     15.6 POLYGON ((8.399011 12.82706, 8.…
 8 Cross River         1446       3492     41.4 MULTIPOLYGON (((8.818036 5.6935…
 9 Plateau             1332       4701     28.3 POLYGON ((8.820398 10.38392, 8.…
10 Oyo                 1329       4085     32.5 POLYGON ((4.024729 7.664918, 4.…
# … with 27 more rows

#sum of LGAs by states 
nga_wp %>% 
  group_by(ADM1_EN) %>% 
  summarise(count = n())%>% 
    arrange(desc(count))

Simple feature collection with 37 features and 2 fields
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS:  WGS 84
# A tibble: 37 × 3
   ADM1_EN   count                                                      geometry
   <chr>     <int>                                                <GEOMETRY [°]>
 1 Kano         44 POLYGON ((8.727456 12.21461, 8.72491 12.21429, 8.721373 12.2…
 2 Katsina      34 POLYGON ((8.3992 13.0758, 8.39043 13.08745, 8.38292 13.09174…
 3 Oyo          33 POLYGON ((4.024729 7.664918, 4.039038 7.684978, 4.056281 7.6…
 4 Akwa Ibom    31 MULTIPOLYGON (((7.530807 5.150259, 7.531415 5.146801, 7.5325…
 5 Osun         30 POLYGON ((4.910021 7.841812, 4.911101 7.85011, 4.914135 7.85…
 6 Borno        27 POLYGON ((14.58718 11.75277, 14.58861 11.75334, 14.59292 11.…
 7 Imo          27 POLYGON ((7.422786 5.583626, 7.425965 5.585613, 7.426281 5.5…
 8 Jigawa       27 POLYGON ((8.399011 12.82706, 8.390081 12.82528, 8.38304 12.8…
 9 Delta        25 POLYGON ((5.985599 5.124185, 5.99217 5.117613, 5.995821 5.11…
10 Niger        25 POLYGON ((7.250727 10.03942, 7.240785 10.03998, 7.232374 10.…
# … with 27 more rows

Reference

LOH, S.Y (2022), Take-home Exercise 1: Geospatial Analytics for Social Good

https://lohsiying.netlify.app/take_home_ex/ex1/take-home-ex1.html